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CS 369G: Algorithmic Techniques for Big Data

Designing algorithms for efficient processing of large data sets poses unique challenges. This course will discuss algorithmic paradigms that have been developed to efficiently process data sets that are much larger than available memory. We will cover streaming algorithms and sketching methods that produce compact data structures, dimension reduction methods that preserve geometric structure, efficient algorithms for numerical linear algebra, graph sparsification methods, as well as impossibility results for these techniques. Prerequisite: a strong background in algorithms and probability.
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
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